Feature Discovery with Type Extension Trees

Authors:
Paolo Frasconi;Manfred Jaeger;Andrea Passerini
Affiliations:
Dipartimento di Sistemi e Informatica, Universitá degli Studi di Firenze, Italy;Department for Computer Science, Aalborg University, Denmark;Dipartimento di Sistemi e Informatica, Universitá degli Studi di Firenze, Italy
Venue:
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Year:
2008

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Abstract

We are interested in learning complex combinatorial features from relational data. We rely on an expressive and general representation language whose semantics allows us to express many features that have been used in different statistical relational learning settings. To avoid expensive exhaustive search over the space of relational features, we introduce a heuristic search algorithm guided by a generalized relational notion of information gain and a discriminant function. The algorithm succesfully finds interesting and interpretable features on artificial and real-world relational learning problems.